1. Technical Field
The technology of this disclosure pertains generally to 3D image segmentation, and more particularly to clinical 3D image diagnostics of blood vessels and aneurysms.
2. Background Discussion
A number of 3D image segmentation methods have been developed; however, these methods rarely achieve satisfactory performance for 3D segmentation due to the complex structure, limited image resolution, and generation of unwanted artifacts. Several efforts have been made to adapt existing methods, such as active contour methods, to 3D segmentation by leveraging specific characteristics (i.e., hyper-intensity and tubular-like shapes) of vessels. One segmentation method utilized a ball measurement, in which a ball was used as an initial shape to penalize local widening of contours and to maintain the shape elongation. However, this method can incorrectly penalize local enlargement due to aneurysms and bifurcations, resulting in incomplete segmentation at those locations.
Several vessel-dedicated features have been proposed to produce a force field which drives the contours towards vessel edges. Notable results include the spherical flux measure, optimally oriented flux, the Hessian-based vessel filter, and so forth. Recently, a non-parametric geodesic active contours (GAC) approach was proposed which incorporates high-order multiscale features to model the region of interest.
Despite performance improvements demonstrated by these methods dedicated to 3D segmentation, setting proper values for a set of parameters to achieve optimized results remains problematic for users. One approach, referred to as ITK-SNAP, provides an open-source software which utilizes a user-friendly interface and feedback to facilitate parameter selection for medical image segmentation. However, the method still requires manual configuration, while the software only allows a single configuration that is globally applied to all voxels in the entire 3D image that often leads to sub-optimal performance.
One of the most popular approaches for segmentation is the application of enhancement filters to individual pixels and then to classify pixels. Several enhancement filters have been proposed which utilize second-order derivatives to distinguish specific shapes, such as having a locally prominent low curvature orientation (i.e., the vessel direction) and planes of high intensity curvature (i.e., the cross-sectional planes). The Hessian matrix is the most common tool to capture tubular structure information. Eigenvalues of a Hessian matrix can discriminate between plane-, blob- and tubular-like structures, and eigenvectors indicate the vessel orientations. An example of a Hessian-matrix based method is a vesselness filter which has been extensively used in practice, owing to its intuitive geometric formulation. The Weingarten matrix is a less popular alternative to the Hessian matrix.
Instead of analyzing second-order derivatives, another group of methods exploit the local distribution of gradient vectors. One instance performs an eigenvalue analysis on the covariance matrix of gradient vectors. Another instance leveraged a vector field obtained from gradient vector flow (GVF) diffusion. Still another proposed approach is optimally oriented flux (OOF) which relies on the measure of gradient flux through the boundary of local spheres. Comparing to Hessian-based filters, OOF is claimed to be more accurate and less sensitive to disturbances from adjacent structures.
One proposal estimates the eigenvalue distribution of the covariance matrix of gradient vectors via Expectation Maximization (EM). Support Vector Machines (SVM) operating on the Hessian's eigenvalues have been used to discriminate between target and nontarget pixels. In another proposal, rotational features were computed at each pixel using steerable filters and fed to an SVM to classify pixels as vessels or not. Inspired by use of these rotational features, a series of improvements were made by including more filters (i.e., vesselness and OOF) in addition to steerable filters and leveraging more advanced machine learning techniques.
It should be noted that both handcrafted and learned filters rely on image gradients or high-order derivatives. Therefore the results are sensitive to noise and often too weak to discriminate target pixels in low contrast regions.
One application of these 3D image processing techniques is in applications such as vascular imaging and aneurysm growth detection and segmentation. Current angiogram outputs inevitably contain noise and exhibit inhomogeneous contrast. The intensity of some vessels (particularly narrow vessels) could differ from the background by as little as four grey levels, yet the background noise standard deviation is 2.3 grey levels. As a result, most if not all, existing filters lose effectiveness in those low contrast, low SNR regions. In addition, vascular filters usually provide weak responses around vascular borders, yielding difficulties in precisely localizing the true boundary of a vessel tube. Imprecise boundary localization could consequently result in inaccurate quantification of pathologies and diagnosis. Based on the segmented vessels, aneurysms can be detected a posteriori as local radius increases, or by 3D curvature analysis. Experiments from the literature suggest local radius and volume increase as possible indicators of most prominent aneurysms. In addition, aneurysm could also be detected by specific Hessian-based aneurysm detectors. Other techniques dedicated to aneurysm detection and segmentation include learning models and volume-based estimation.
Accordingly, a need exists for advanced segmentation approaches, which provide for increased accuracy while not requiring complex parameter modifications by a highly trained clinician. The present disclosure fulfills that need and provides additional benefits for performing 3D image segmentation, such as for 3D imaging for blood vessels, aneurysms, tumors, thromboses, inflammations and so forth.